The present invention relates to an electric motor monitoring system, and more particularly, to an apparatus and method to detect a stator turn fault in an AC motor.
Electric motors, such as three-phase AC induction motors, are used in a variety of commercial and industrial environments. Refrigeration systems, printing presses, assembly lines and a myriad of other applications use such motors. Regardless of the application, timely detection of a motor fault is of utmost importance. Generally, a motor fault is not detected until complete breakdown of the electric motor, thereby, creating a situation marred with undue cost, down-time delay in repairs, as well as, potential hazardous conditions. As a result, it is necessary to efficiently and effectively detect a motor fault, specifically, a stator turn fault prior to complete breakdown of the electric motor.
State-of-the-art monitoring techniques for electric motors do not sufficiently provide for pre-breakdown detection of a stator turn fault. Typically, electric motor monitoring systems detect motor faults resulting from a defect in the rotor and defects that can only be detected during rotation. These known systems interpret changes in the harmonics of the spectrum generated by the rotor during rotation. The stator of an electric motor, however, is a stationary member and, therefore, a defect does not produce additional harmonics. Hence, application of such conventional techniques to detect a motor fault resulting from a breakdown or malfunction in the stator windings of an electric motor is not possible.
Prior art stator fault detection systems are also wrought with problems. These known stator turn fault detection systems are inefficient and time-consuming. Many methods use the model of an ideal, balanced motor, and fail because of the non-idealities in an actual machine. In one method, it is necessary to derive a xe2x80x9clook-up tablexe2x80x9d comprising data for a plethora of operating conditions of the electric motor. In order to accumulate sufficient data to make the look-up table useful, it is necessary to run the electric motor through numerous cycles under various operating conditions. To then determine the presence of a stator turn fault, operating data gathered at a particular instant in time during normal operation of the electric motor is compared to the data of the look-up table for the electric motor under similar operating parameters. The effectiveness of determining a turn fault depends upon the scope and extent of the look-up table. The greater the scope and extent of the look up table, the greater the cost. Thus, application of known stator turn fault detection schemes are significantly limited and/or costly. Therefore, the look up table may not include data for all possible operating conditions.
Further, these prior art stator fault detection systems use a Weighted Fast Fourier Transform (WFFT). The WFFT requires several sets of data over an interval of time to perform the transformation.
It would therefore be desirable to design a stator turn fault detection apparatus and method that detects motor faults associated with the stator of the electric motor as well as obviate the need to produce a look up table to store each and every operating condition contemplated.
The present invention relates to a method and system to eliminate the need for generating an operating parameter reference table to detect motor faults and enable the use of an on-the-fly computer network that overcomes the aforementioned problems.
The present invention provides a way to readily replace the aforementioned look-up table and the need to estimate or generate each and every operating parameter of a motor by employing a feed forward neural network. The present invention includes obtaining voltage and current data of an electric motor under known healthy operating conditions. A computer program is provided to transform the existing voltage and current data such that characteristic data can be readily obtained and stored for later use. The system also includes a means to obtain instantaneous data from an electric motor under actual operating conditions. The instantaneous data is then compared to estimated data that is generated by the feed forward neural network. Based upon the comparison of the aforementioned data, an onset of a stator turn fault in the electric motor can be accurately identified.
Therefore, in accordance with one aspect of the invention, a stator turn fault detector for an electric motor is disclosed. The detector includes a plurality of sensors to obtain current and voltage signals supplying the electric motor. Sequence components of the current and voltage data are then calculated by a processor connected to the plurality of sensors as well as connected to a feed forward neural network. The feed forward neural network receives the current and voltage values, or at least a portion thereof, and calculates estimated values which are output to a comparator. Based upon an analysis of the estimated value and instantaneous values, an onset of a stator turn fault can be determined.
In accordance with another aspect of the invention, a stator turn fault detection system is disclosed that includes a microprocessor and a computer readable storage medium. When instructed by a computer program stored on the computer readable storage medium, the microprocessor receives, through at least one input, fundamental frequency data having a positive and negative sequence component of line voltage and a positive sequence component of line current supplying an electric motor. The microprocessor when instructed by the computer program further initiates a feed forward neural network that, based upon the aforementioned data, determines an estimated negative sequence component of the line current. The present invention also includes at least one output to output the estimated negative sequence component of the line current.
In yet another aspect of the present invention, an apparatus for detecting a stator turn fault in an electric motor includes a means for receiving line voltage signals and line current signals from an electric motor known to be operating properly. A transformation means for determining the sequence components of at least a portion of the voltage and current signals, continuously in time is also provided. The present invention further includes a means for outputting estimated current values based upon the aforementioned sequence components. The estimated current values are subsequently compared in real-time to instantaneously acquired current values through a comparing means.
In accordance with another aspect of the invention, a computer program is disclosed to detect a stator turn fault in an electric motor. When executed the computer program will cause a computer to acquire fundamental frequency data of an electric motor during good working order operation. The computer program will further cause at least a portion of the fundamental frequency data to be input to a feed forward neural network having a number of weights. The computer will then train the feed forward neural network to converge each weight to a value and store the value in memory. The computer will also obtain fundamental frequency data from the electric motor during instantaneous operation. The instantaneous fundamental frequency parameter is then input to the feed forward neural network. The computer will then obtain an estimated fundamental frequency parameter of instantaneous operation of the motor and compare that parameter to the instantaneous fundamental frequency parameter to determine a turn fault in the AC motor.
In a further aspect of the present invention, an apparatus to detect a stator turn fault in an AC induction motor is disclosed. The apparatus includes at least two current sensors to obtain at least two AC motor current signals as well as at least two voltage sensors for obtaining AC motor voltage signals. The AC motor current signals and the AC motor voltage signals are input to an analog-to-digital converter to produce digitized current signals and digitized voltage signals. A microprocessor is also contemplated in the present invention to interpret the digitized signals to calculate fundamental frequency sequence parameters of AC motor operation. Estimated fundamental frequency parameters of operation of the AC motor are determined by a feed forward neural network.
In accordance with yet another aspect of the present invention, a method is disclosed for detecting the presence of a stator turn fault in an electric motor which includes the steps of acquiring fundamental frequency training parameters from the electric motor during healthy operating conditions of the electric motor. The method of detecting a stator turn fault also includes determining sequence phasors from the fundamental frequency training parameters to determine estimated fundamental frequency values of normal operation of the electric motor and determining those estimated fundamental frequency values. Preferably, the method includes the additional steps of acquiring instantaneous fundamental frequency values from the electric motor while in service. The method next compares the instantaneous fundamental frequency values to the estimated fundamental frequency values of operation to determine a fault value and indicate the fault value based on the comparison. The fault value being indicative of the presence of a stator turn fault within the electric motor.
In a further aspect of the present invention a method for determining the presence of a stator turn fault in an AC induction motor includes the steps of selecting the sequence phasor parameters of a feed forward neural network and training the feed forward neural network to learn a model of the AC induction motor under healthy operating conditions. The method then acquires a measured value of the AC induction motor while in service and compares the measured value taken from the in-service electric motor to an estimated value of AC induction motor operation. The method then repeats the aforementioned steps until a turn fault value that exceeds a vigilance is detected. When such a turn fault value is detected, the method indicates the presence of a stator turn fault in the AC induction motor to a user.